docs: def _fisher_jenks_means(values, classes=5, sort=True):
      """
      Jenks Optimal (Natural Breaks) algorithm implemented in Python.
  
      Notes
      -----
      The original Python code comes from here:
      http://danieljlewis.org/2010/06/07/jenks-natural-breaks-algorithm-in-python/
      and is based on a JAVA and Fortran code available here:
      https://stat.ethz.ch/pipermail/r-sig-geo/2006-March/000811.html
  
      Returns class breaks such that classes are internally homogeneous while
      assuring heterogeneity among classes.
  
      """
      if sort:
          values.sort()
      n_data = len(values)
      mat1 = np.zeros((n_data + 1, classes + 1), dtype=np.int32)
      mat2 = np.zeros((n_data + 1, classes + 1), dtype=np.float32)
      mat1[1, 1:] = 1
      mat2[2:, 1:] = np.inf
  
      v = np.float32(0)
      for l in range(2, len(values) + 1):
          s1 = np.float32(0)
          s2 = np.float32(0)
          w = np.float32(0)
          for m in range(1, l + 1):
              i3 = l - m + 1
              val = np.float32(values[i3 - 1])
              s2 += val * val
              s1 += val
              w += np.float32(1)
              v = s2 - (s1 * s1) / w
              i4 = i3 - 1
              if i4 != 0:
                  for j in range(2, classes + 1):
                      if mat2[l, j] >= (v + mat2[i4, j - 1]):
                          mat1[l, j] = i3
                          mat2[l, j] = v + mat2[i4, j - 1]
          mat1[l, 1] = 1
          mat2[l, 1] = v
  
      k = len(values)
  
      kclass = np.zeros(classes + 1, dtype=values.dtype)
      kclass[classes] = values[len(values) - 1]
      kclass[0] = values[0]
      for countNum in range(classes, 1, -1):
          pivot = mat1[k, countNum]
          id = int(pivot - 2)
          kclass[countNum - 1] = values[id]
          k = int(pivot - 1)
      return kclass